Github user thunterdb commented on a diff in the pull request:
https://github.com/apache/spark/pull/9907#discussion_r49002633
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
---
@@ -109,4 +109,55 @@ class RegressionMetricsSuite extends SparkFunSuite
with MLlibTestSparkContext {
"root mean squared error mismatch")
assert(metrics.r2 ~== 1.0 absTol 1E-5, "r2 score mismatch")
}
+
+ test("regression metrics with same(1.0) weight samples") {
+ val predictionAndObservationWithWeight = sc.parallelize(
+ Seq((2.25, 3.0, 1.0), (-0.25, -0.5, 1.0), (1.75, 2.0, 1.0), (7.75,
7.0, 1.0)), 2)
+ val metrics = new RegressionMetrics(predictionAndObservationWithWeight)
+ assert(metrics.explainedVariance ~== 8.79687 absTol 1E-5,
+ "explained variance regression score mismatch")
+ assert(metrics.meanAbsoluteError ~== 0.5 absTol 1E-5, "mean absolute
error mismatch")
+ assert(metrics.meanSquaredError ~== 0.3125 absTol 1E-5, "mean squared
error mismatch")
+ assert(metrics.rootMeanSquaredError ~== 0.55901 absTol 1E-5,
+ "root mean squared error mismatch")
+ assert(metrics.r2 ~== 0.95717 absTol 1E-5, "r2 score mismatch")
+ }
+
+
+ /**
+ * The following values are hand calculated using the formula:
+ *
[[https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Reliability_weights]]
+ * preds = c(2.25, -0.25, 1.75, 7.75)
+ * obs = c(3.0, -0.5, 2.0, 7.0)
+ * weights = c(0.1, 0.2, 0.15, 0.05)
+ * count = 4
+ *
+ * Weighted metrics can be calculated with
MultivariateStatisticalSummary.
+ * (observations, observations - predictions)
+ * mean (1.7, 0.05)
+ * variance (7.3, 0.3)
+ * numNonZeros (0.5, 0.5)
+ * max (7.0, 0.75)
+ * min (-0.5, -0.75)
+ * normL2 (2.0, 0.32596)
+ * normL1 (1.05, 0.2)
+ *
+ * explainedVariance: sum((preds - 1.7)^2) / count = 10.1775
+ * meanAbsoluteError: normL1(1) / count = 0.05
+ * meanSquaredError: normL2(1)^2 / count = 0.02656
+ * rootMeanSquaredError: sqrt(meanSquaredError) = 0.16298
+ * r2: 1 - normL2(1)^2 / (variance(0) * (count - 1)) = 0.9951484
--- End diff --
Same thing for this formula.
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